1Dalhousie University, Department of Oceanography, Halifax, Canada
2Department de Biologié, Université Laval, Québec, Québec, Canada
3Demersal and Benthic Sciences Division, Maurice-Lamontagne Institute, Fisheries and Oceans Canada, Mont-Joli, Québec, Canada
4Arctic and Aquatic Research Division, Fisheries and Oceans Canada, Winnipeg, Manitoba, Canada
5Institute of Marine Research, His, Norway
6Center of Earth Observation Science, University of Manitoba, Winnipeg, Manitoba, Canada
7Centre for Arctic Knowledge and Exploration, Canadian Museum of Nature, Ottawa, ON, Canada

1 Introduction

1.1 Current knowledge

1.1.1 Present

  • Evidence suggests that many Arctic coasts should support seaweed
  • In Canada, kelp has been reported and documented along Arctic and subarctic coastlines
  • However, baseline measures of the extent of kelp communities are missing in much of the region

1.1.2 Future

  • Rapid environmental changes, such as declining sea ice, increased ocean temperatures, and freshwater inputs are occurring along Canadian coasts
  • Research suggests northern expansion of kelp forests with climate change
  • Therefore, the relationships between environmental factors and the presence of kelp forests in the Canadian Arctic are critical to understand

1.1.3 Existing database

1.1.4 ArcticKelp project

  • This dive research conducted throughout the Canadian Arctic in 2014 - 2019
    • 5 - 20 m photograph quadrats

1.1.4.1 Campaigns

1.1.4.2 Mean cover

1.2 Questions

  • Is it possible to model the distribution (suitability + abundance) of different functional groups of kelps in the Arctic given our current knowledge?
    • Total kelp cover
    • Laminariales (Laminaria sp. + Sacharina sp.)
    • Agarum
    • Alaria
  • How accurate are the models?
  • Which environmental variables are the most important?
  • What might future distributions look like?

2 Methods

2.1 Data


(Assis et al., 2018; Tyberghein et al., 2012)

2.1.1 Bio-ORACLE

  • Geophysical, biotic, and abiotic environmental variables
  • Collection from many different datasets
  • Surface and benthic coverage
  • Data from 2000 - 2014 for most
  • Single values per pixel; min, mean, max, and range for most
  • 5 arcdegree spatial resolution (~9.2 km at the equator)

2.1.2 Variables (32)

  • Temperature
  • Salinity
  • Ice thickness (surface only)
  • Current velocity
  • Photosynthetically active radiation (PAR; surface only)
  • Dissolve oxygen
  • Iron
  • Nitrate
  • Phosphate

2.1.3 Final variables (8)

  • Bottom temperature; long-term minimum
  • Bottom temperature; long-term maximum
  • Surface temperature; long-term maximum
  • Bottom salinity; long-term maximum
  • Ice thickness; long-term minimum
  • Bottom iron; long-term maximum
  • Bottom phosphate; long-term maximum
  • Bottom current velocity; long-term minimum

2.1.4 Future variables (6)

  • Bottom temperature; long-term minimum
  • Bottom temperature; long-term maximum
  • Surface temperature; long-term maximum
  • Bottom salinity; long-term maximum
  • Ice thickness; long-term minimum
  • Bottom current velocity; long-term minimum

2.2 Ensemble model (suitability)

  • Ensemble performed with default BIOMOD2 settings (Thuiller et al., 2020)
  • Models: MAXENT (Phillips), GLM, ANN, RF, GAM (Goldsmit et al., 2020)
  • Random-pseudo absence (PA); 1000 points; 5 repetitions
  • 70/30 train test split
  • Modeled for entire Arctic ecoregion
  • Results cropped to Eastern Canadian Arctic

2.3 Random forest model (abundance)

  • 200 trees; 1000 repetitions
  • 70/30 train test split
  • Modeled only for Eastern Canadian Arctic

3 Results

3.1 Ensemble

3.2 Random Forest

3.2.1 Confidence

3.2.1.1 Laminariales

3.2.1.2 Agarum

3.2.1.3 Alaria

3.2.1.4 Total cover

3.2.2 Top variables

3.2.2.1 Laminariales

Data layer % Inc. MSE
Latitude 40
Longitude 27
Photosynthetically available radiation (mean) 23
Ice fraction 0
Chlorophyll concentration (mean at min depth) 0
Evap minus Precip over ocean 0

3.2.2.2 Agarum

Data layer % Inc. MSE
Ice thickness (cell average) 66
Light at bottom (mean at min depth) 53
Iron concentration (mean at min depth) 46
Net Downward Heat Flux 0
shear 0
total flux at ocean surface 0

3.2.2.3 Alaria

Data layer % Inc. MSE
total flux at ocean surface 8
non-solar heat flux at ocean surface 8
Sea Water Salinity 7
Light at bottom (mean at min depth) 0
kinetic energy 0
daily dynamic ice prod. 0

3.2.2.4 Total kelp

Data layer % Inc. MSE
Sea water temperature (mean at min depth) 92
Dissolved oxygen concentration (mean at min depth) 86
Ice divergence 80
Sea Water Y Velocity 0
kinetic energy 0
heat fluxes causing bottom ice melt 0

3.2.3 Projections

  • Note that the colour scales are not the same between figures

3.2.3.1 Laminariales

3.2.3.2 Agarum

3.2.3.3 Alaria

3.2.3.4 Total cover

3.2.4 Linear regression

4 Conclusions

  • Ensemble

  • RF

  • Contrast

  • Why

  • There should be quite a lot of kelp in the Arctic

  • There are different spatial projections for different groups

  • Alaria projections are likely incorrect and require more data

  • These projections provide a good platform for deciding future sampling locations

5 Further work

6 Acknowledgements

  • This research was undertaken thanks in part to funding from the Canada First Research Excellence Fund, through the Ocean Frontier Institute.

References

Assis, J., Tyberghein, L., Bosch, S., Verbruggen, H., Serrão, E. A., and De Clerck, O. (2018). Bio-oracle v2. 0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography 27, 277–284.

Goldsmit, J., McKindsey, C. W., Schlegel, R. W., Stewart, D. B., Archambault, P., and Howland, K. L. (2020). What and where? Predicting invasion hotspots in the arctic marine realm. Global change biology 26, 4752–4771.

Thuiller, W., Georges, D., Engler, R., and Breiner, F. (2020). Biomod2: Ensemble platform for species distribution modeling. Available at: https://CRAN.R-project.org/package=biomod2.

Tyberghein, L., Verbruggen, H., Pauly, K., Troupin, C., Mineur, F., and De Clerck, O. (2012). Bio-oracle: A global environmental dataset for marine species distribution modelling. Global ecology and biogeography 21, 272–281.